{"id":476792,"date":"2023-08-09T07:36:15","date_gmt":"2023-08-09T07:36:15","guid":{"rendered":""},"modified":"2023-09-05T11:13:27","modified_gmt":"2023-09-05T11:13:27","slug":"dependency-parsing","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/tr\/wiki\/dependency-parsing\/","title":{"rendered":"Ba\u011f\u0131ml\u0131l\u0131k ayr\u0131\u015ft\u0131rma"},"content":{"rendered":"<p>Ba\u011f\u0131ml\u0131l\u0131k ayr\u0131\u015ft\u0131rma, Do\u011fal Dil \u0130\u015fleme (NLP) alan\u0131nda kullan\u0131lan ve bir c\u00fcmlenin gramer yap\u0131s\u0131n\u0131n anla\u015f\u0131lmas\u0131na ve temsil edilmesine yard\u0131mc\u0131 olan \u00f6nemli bir tekniktir. NLP&#039;deki makine \u00e7evirisi, bilgi \u00e7\u0131karma ve soru cevaplama sistemleri gibi \u00e7e\u015fitli uygulamalar\u0131n omurgas\u0131n\u0131 olu\u015fturur.<\/p>\n<h2>Ba\u011f\u0131ml\u0131l\u0131k Ayr\u0131\u015ft\u0131rman\u0131n Tarihsel Ba\u011flam\u0131 ve \u0130lk S\u00f6zleri<\/h2>\n<p>Ba\u011f\u0131ml\u0131l\u0131k ayr\u0131\u015ft\u0131rma bir kavram olarak teorik dilbilimin ilk y\u0131llar\u0131nda ortaya \u00e7\u0131km\u0131\u015ft\u0131r. \u0130lk kavramlar, eski bir Hint dilbilgisi uzman\u0131 olan Panini&#039;ye kadar uzanan geleneksel dilbilgisi teorilerinden ilham ald\u0131. Bununla birlikte, ba\u011f\u0131ml\u0131l\u0131k dilbilgisinin modern bi\u00e7imi \u00f6ncelikle 20. y\u00fczy\u0131lda dilbilimci Lucien Tesni\u00e8re taraf\u0131ndan geli\u015ftirildi.<\/p>\n<p>Tesni\u00e8re, &quot;ba\u011f\u0131ml\u0131l\u0131k&quot; terimini, \u00f6l\u00fcm\u00fcnden sonra 1959&#039;da yay\u0131nlanan ufuk a\u00e7\u0131c\u0131 \u00e7al\u0131\u015fmas\u0131 &quot;Yap\u0131sal S\u00f6zdiziminin Unsurlar\u0131&quot;nda tan\u0131tt\u0131. Kelimeler aras\u0131ndaki s\u00f6zdizimsel ili\u015fkilerin, se\u00e7men temelli yakla\u015f\u0131mlar yerine ba\u011f\u0131ml\u0131l\u0131k kavram\u0131 kullan\u0131larak en iyi \u015fekilde yakalanabilece\u011fini savundu.<\/p>\n<h2>Konuyu Geni\u015fletmek: Ba\u011f\u0131ml\u0131l\u0131k Ayr\u0131\u015ft\u0131rma Hakk\u0131nda Ayr\u0131nt\u0131l\u0131 Bilgi<\/h2>\n<p>Ba\u011f\u0131ml\u0131l\u0131k ayr\u0131\u015ft\u0131rma, bir c\u00fcmledeki kelimeler aras\u0131ndaki gramer ili\u015fkilerini tan\u0131mlamay\u0131 ve bunlar\u0131, her d\u00fc\u011f\u00fcm\u00fcn bir kelimeyi temsil etti\u011fi ve her kenar\u0131n kelimeler aras\u0131ndaki bir ba\u011f\u0131ml\u0131l\u0131k ili\u015fkisini temsil etti\u011fi bir a\u011fa\u00e7 yap\u0131s\u0131 olarak temsil etmeyi ama\u00e7lar. Bu yap\u0131larda, bir kelime (kafa) di\u011fer kelimeleri (ba\u011f\u0131ml\u0131 ki\u015filer) y\u00f6netir veya ona ba\u011fl\u0131d\u0131r.<\/p>\n<p>\u00d6rne\u011fin \u015fu c\u00fcmleyi d\u00fc\u015f\u00fcn\u00fcn: &quot;John topu att\u0131.&quot; Ba\u011f\u0131ml\u0131l\u0131k ayr\u0131\u015ft\u0131rma a\u011fac\u0131nda &quot;f\u0131rlat&quot; c\u00fcmlenin k\u00f6k\u00fc (veya ba\u015f\u0131) olurken, &quot;John&quot; ve &quot;top&quot; onun ba\u011f\u0131ml\u0131lar\u0131 olur. Ayr\u0131ca, &quot;top&quot; &quot;top&quot; ve &quot;top&quot; olarak ikiye ayr\u0131labilir; &quot;top&quot; ba\u015f ve &quot;the&quot; ona ba\u011f\u0131ml\u0131d\u0131r.<\/p>\n<h2>Ba\u011f\u0131ml\u0131l\u0131k Ayr\u0131\u015ft\u0131rman\u0131n \u0130\u00e7 Yap\u0131s\u0131: Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/h2>\n<p>Ba\u011f\u0131ml\u0131l\u0131k ayr\u0131\u015ft\u0131rma birka\u00e7 a\u015famadan olu\u015fur:<\/p>\n<ol>\n<li><strong>Tokenle\u015ftirme:<\/strong> Metin tek tek kelimelere veya simgelere b\u00f6l\u00fcnm\u00fc\u015ft\u00fcr.<\/li>\n<li><strong>Konu\u015fma B\u00f6l\u00fcm\u00fc (POS) Etiketleme:<\/strong> Her belirte\u00e7, isim, fiil, s\u0131fat vb. gibi konu\u015fman\u0131n uygun k\u0131sm\u0131yla etiketlenir.<\/li>\n<li><strong>Ba\u011f\u0131ml\u0131l\u0131k \u0130li\u015fkisi Atamas\u0131:<\/strong> Ba\u011f\u0131ml\u0131l\u0131k dilbilgisi kurallar\u0131na g\u00f6re belirte\u00e7ler aras\u0131nda bir ba\u011f\u0131ml\u0131l\u0131k ili\u015fkisi atan\u0131r. \u00d6rne\u011fin \u0130ngilizce&#039;de bir fiilin \u00f6znesi genellikle solunda, nesnesi ise sa\u011f\u0131ndad\u0131r.<\/li>\n<li><strong>A\u011fa\u00e7 Yap\u0131m\u0131:<\/strong> Etiketli kelimelerin d\u00fc\u011f\u00fcmler ve ba\u011f\u0131ml\u0131l\u0131k ili\u015fkilerinin kenarlar oldu\u011fu bir ayr\u0131\u015ft\u0131rma a\u011fac\u0131 olu\u015fturulur.<\/li>\n<\/ol>\n<h2>Ba\u011f\u0131ml\u0131l\u0131k Ayr\u0131\u015ft\u0131rman\u0131n Temel \u00d6zellikleri<\/h2>\n<p>Ba\u011f\u0131ml\u0131l\u0131k ayr\u0131\u015ft\u0131rman\u0131n temel \u00f6zellikleri \u015funlar\u0131 i\u00e7erir:<\/p>\n<ul>\n<li><strong>Y\u00f6nl\u00fcl\u00fck:<\/strong> Ba\u011f\u0131ml\u0131l\u0131k ili\u015fkileri do\u011fas\u0131 gere\u011fi y\u00f6nl\u00fcd\u00fcr, yani ba\u015ftan ba\u011f\u0131ml\u0131ya do\u011fru akarlar.<\/li>\n<li><strong>\u0130kili \u0130li\u015fkiler:<\/strong> Her ba\u011f\u0131ml\u0131l\u0131k ili\u015fkisi yaln\u0131zca iki \u00f6\u011feyi i\u00e7erir; ba\u015f ve ba\u011f\u0131ml\u0131.<\/li>\n<li><strong>Yap\u0131:<\/strong> C\u00fcmlenin hiyerar\u015fik bir g\u00f6r\u00fcn\u00fcm\u00fcn\u00fc sunan a\u011fa\u00e7 benzeri bir yap\u0131 olu\u015fturur.<\/li>\n<li><strong>Ba\u011f\u0131ml\u0131l\u0131k T\u00fcrleri:<\/strong> Lider ile ba\u011f\u0131ml\u0131lar\u0131 aras\u0131ndaki ili\u015fki, \u201c\u00f6zne\u201d, \u201cnesne\u201d, \u201cde\u011fi\u015ftirici\u201d vb. dilbilgisel ili\u015fki t\u00fcrleriyle a\u00e7\u0131k\u00e7a etiketlenir.<\/li>\n<\/ul>\n<h2>Ba\u011f\u0131ml\u0131l\u0131k Ayr\u0131\u015ft\u0131rma T\u00fcrleri<\/h2>\n<p>\u0130ki temel ba\u011f\u0131ml\u0131l\u0131k ayr\u0131\u015ft\u0131rma y\u00f6ntemi t\u00fcr\u00fc vard\u0131r:<\/p>\n<ol>\n<li>\n<p><strong>Grafik Tabanl\u0131 Modeller:<\/strong> Bu modeller bir c\u00fcmle i\u00e7in m\u00fcmk\u00fcn olan t\u00fcm ayr\u0131\u015ft\u0131rma a\u011fa\u00e7lar\u0131n\u0131 olu\u015fturur ve bunlar\u0131 puanlar. En y\u00fcksek puan\u0131 alan a\u011fa\u00e7 se\u00e7ilir. En iyi bilinen grafik tabanl\u0131 model Eisner algoritmas\u0131d\u0131r.<\/p>\n<\/li>\n<li>\n<p><strong>Ge\u00e7i\u015f Tabanl\u0131 Modeller:<\/strong> Bu modeller a\u015famal\u0131 olarak ayr\u0131\u015ft\u0131rma a\u011fa\u00e7lar\u0131 olu\u015fturur. Bir ba\u015flang\u0131\u00e7 konfig\u00fcrasyonuyla ba\u015flarlar ve bir ayr\u0131\u015ft\u0131rma a\u011fac\u0131 t\u00fcretmek i\u00e7in bir dizi eylem (SHIFT, REDUCE gibi) uygularlar. Ge\u00e7i\u015f tabanl\u0131 modelin bir \u00f6rne\u011fi Arc standart algoritmas\u0131d\u0131r.<\/p>\n<\/li>\n<\/ol>\n<h2>Ba\u011f\u0131ml\u0131l\u0131k Ayr\u0131\u015ft\u0131rman\u0131n Kullan\u0131m Yollar\u0131, Sorunlar ve \u00c7\u00f6z\u00fcmleri<\/h2>\n<p>Ba\u011f\u0131ml\u0131l\u0131k ayr\u0131\u015ft\u0131rma, a\u015fa\u011f\u0131dakiler de dahil olmak \u00fczere NLP uygulamalar\u0131nda yayg\u0131n olarak kullan\u0131l\u0131r:<\/p>\n<ul>\n<li><strong>Makine \u00c7evirisi:<\/strong> Kaynak dildeki gramer ili\u015fkilerinin belirlenmesine ve bunlar\u0131n \u00e7evrilmi\u015f metinde korunmas\u0131na yard\u0131mc\u0131 olur.<\/li>\n<li><strong>Bilgi \u00c7\u0131karma:<\/strong> Metnin anlam\u0131n\u0131 anlamaya ve yararl\u0131 bilgiler \u00e7\u0131karmaya yard\u0131mc\u0131 olur.<\/li>\n<li><strong>Duygu Analizi:<\/strong> Ba\u011f\u0131ml\u0131l\u0131klar\u0131 belirleyerek bir c\u00fcmlenin duygusunu daha do\u011fru bir \u015fekilde anlamaya yard\u0131mc\u0131 olabilir.<\/li>\n<\/ul>\n<p>Ancak ba\u011f\u0131ml\u0131l\u0131k ayr\u0131\u015ft\u0131rma baz\u0131 zorluklar\u0131 da beraberinde getirir:<\/p>\n<ul>\n<li><strong>Belirsizlik:<\/strong> Dildeki belirsizlik birden fazla ge\u00e7erli ayr\u0131\u015ft\u0131rma a\u011fac\u0131na yol a\u00e7abilir. Bu t\u00fcr belirsizlikleri \u00e7\u00f6zmek zorlu bir i\u015ftir.<\/li>\n<li><strong>Verim:<\/strong> Ayr\u0131\u015ft\u0131rma, \u00f6zellikle uzun c\u00fcmleler i\u00e7in hesaplama a\u00e7\u0131s\u0131ndan yo\u011fun olabilir.<\/li>\n<\/ul>\n<p>\u00c7\u00f6z\u00fcm yakla\u015f\u0131mlar\u0131:<\/p>\n<ul>\n<li><strong>Makine \u00f6\u011frenme:<\/strong> Birden fazla ayr\u0131\u015ft\u0131rma a\u011fac\u0131 aras\u0131ndaki belirsizli\u011fi ortadan kald\u0131rmak i\u00e7in makine \u00f6\u011frenimi teknikleri kullan\u0131labilir.<\/li>\n<li><strong>Optimizasyon Algoritmalar\u0131:<\/strong> Ayr\u0131\u015ft\u0131rma s\u00fcrecini optimize etmek i\u00e7in etkili algoritmalar geli\u015ftirilmi\u015ftir.<\/li>\n<\/ul>\n<h2>Benzer Terimlerle Kar\u015f\u0131la\u015ft\u0131rmalar<\/h2>\n<table>\n<thead>\n<tr>\n<th><\/th>\n<th>Ba\u011f\u0131ml\u0131l\u0131k Ayr\u0131\u015ft\u0131rma<\/th>\n<th>Se\u00e7im B\u00f6lgesi Ayr\u0131\u015ft\u0131rma<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Odak<\/td>\n<td>\u0130kili ili\u015fkiler (kafaya ba\u011fl\u0131)<\/td>\n<td>C\u00fcmle bile\u015fenleri<\/td>\n<\/tr>\n<tr>\n<td>Yap\u0131<\/td>\n<td>Her kelime i\u00e7in bir ebeveynin m\u00fcmk\u00fcn oldu\u011fu a\u011fa\u00e7 benzeri yap\u0131<\/td>\n<td>A\u011fa\u00e7 benzeri yap\u0131, bir kelime i\u00e7in birden fazla ebeveyne izin verir<\/td>\n<\/tr>\n<tr>\n<td>\u0130\u00e7in kullan\u0131l\u0131r<\/td>\n<td>Bilgi \u00e7\u0131karma, makine \u00e7evirisi, duygu analizi<\/td>\n<td>C\u00fcmle olu\u015fturma, makine \u00e7evirisi<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Ba\u011f\u0131ml\u0131l\u0131k Ayr\u0131\u015ft\u0131rmayla \u0130lgili Gelecek Perspektifleri<\/h2>\n<p>Makine \u00f6\u011frenimi ve yapay zekadaki ilerlemelerle ba\u011f\u0131ml\u0131l\u0131k ayr\u0131\u015ft\u0131rman\u0131n daha do\u011fru ve verimli hale gelmesi bekleniyor. Transformat\u00f6rler ve tekrarlayan sinir a\u011flar\u0131 (RNN&#039;ler) gibi derin \u00f6\u011frenme y\u00f6ntemleri bu alana \u00f6nemli katk\u0131lar sa\u011fl\u0131yor.<\/p>\n<p>Dahas\u0131, \u00e7ok dilli ve diller aras\u0131 ba\u011f\u0131ml\u0131l\u0131\u011f\u0131n ayr\u0131\u015ft\u0131r\u0131lmas\u0131 b\u00fcy\u00fcyen bir ara\u015ft\u0131rma alan\u0131d\u0131r. Bu, sistemlerin daha az kaynakla dilleri verimli bir \u015fekilde anlamas\u0131na ve \u00e7evirmesine olanak tan\u0131yacakt\u0131r.<\/p>\n<h2>Proxy Sunucular\u0131 ve Ba\u011f\u0131ml\u0131l\u0131k Ayr\u0131\u015ft\u0131rma<\/h2>\n<p>Proxy sunucular\u0131 ba\u011f\u0131ml\u0131l\u0131k ayr\u0131\u015ft\u0131rmayla do\u011frudan etkile\u015fime girmese de, bu tekni\u011fi kullanan NLP g\u00f6revlerini kolayla\u015ft\u0131rmak i\u00e7in kullan\u0131labilirler. \u00d6rne\u011fin, ba\u011f\u0131ml\u0131l\u0131k ayr\u0131\u015ft\u0131rma da dahil olmak \u00fczere NLP modellerini e\u011fitmek amac\u0131yla web verilerini kaz\u0131mak i\u00e7in bir proxy sunucusu kullan\u0131labilir. Ayn\u0131 zamanda bir anonimlik katman\u0131 sa\u011flayarak bu i\u015flemleri y\u00fcr\u00fcten ki\u015fi veya kurulu\u015flar\u0131n gizlili\u011fini korur.<\/p>\n<h2>\u0130lgili Ba\u011flant\u0131lar<\/h2>\n<ol>\n<li><a href=\"https:\/\/nlp.stanford.edu\/pubs\/Dozat2017Dependency.pdf\" target=\"_new\" rel=\"noopener nofollow\">Stanford&#039;un Evrensel Ba\u011f\u0131ml\u0131l\u0131k Ayr\u0131\u015ft\u0131rma makalesi<\/a><\/li>\n<li><a href=\"https:\/\/spacy.io\/api\/dependencyparser\" target=\"_new\" rel=\"noopener nofollow\">Spacy&#039;nin Ba\u011f\u0131ml\u0131l\u0131k Ayr\u0131\u015ft\u0131rma belgeleri<\/a><\/li>\n<li><a href=\"https:\/\/www.sketchengine.eu\/user-guide\/user-manual\/corpora-by-languages\/dependency-grammar\/\" target=\"_new\" rel=\"noopener nofollow\">Ba\u011f\u0131ml\u0131l\u0131k Dilbilgisine Giri\u015f<\/a><\/li>\n<li><a href=\"https:\/\/www.researchgate.net\/publication\/227988873_Lucien_Tesniere&#039;s_&#039;Elements_de_syntaxe_structurale&#039;_Fifty_years_on_1959-2009\" target=\"_new\" rel=\"noopener nofollow\">Lucien Tesni\u00e8re ve Ba\u011f\u0131ml\u0131l\u0131k Dilbilgisi<\/a><\/li>\n<\/ol>","protected":false},"featured_media":468201,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-476792","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Dependency Parsing: An Informative Guide<\/mark>","faq_items":[{"question":"What is Dependency Parsing?","answer":"<p>Dependency Parsing is a technique used in Natural Language Processing (NLP) to understand and represent the grammatical structure of a sentence. It forms the core of various applications in NLP, such as machine translation, information extraction, and question-answering systems.<\/p>"},{"question":"Who introduced the concept of Dependency Parsing?","answer":"<p>The concept of Dependency Parsing was introduced by Lucien Tesni\u00e8re in his work \"Elements of Structural Syntax,\" published in 1959. The idea originates from traditional grammatical theories, with its modern form developed by Tesni\u00e8re in the 20th century.<\/p>"},{"question":"What is the process of Dependency Parsing?","answer":"<p>Dependency Parsing involves several stages: Tokenization (dividing the text into individual words), Part-of-Speech (POS) Tagging (labeling each word with its part of speech), Dependency Relation Assignment (assigning a dependency relation between words based on the rules of dependency grammar), and Tree Construction (constructing a parse tree with words as nodes and dependency relations as edges).<\/p>"},{"question":"What are the key features of Dependency Parsing?","answer":"<p>Key features of Dependency Parsing include directionality (dependency relations are directional), binary relations (each dependency relation involves only two elements), a tree-like structure, and explicit labeling of dependency types (the relation between the head and its dependents is explicitly labeled with grammatical relation types).<\/p>"},{"question":"What are the different types of Dependency Parsing methods?","answer":"<p>There are primarily two types of Dependency Parsing methods: Graph-Based Models, which generate and score all possible parse trees for a sentence, and Transition-Based Models, which build parse trees incrementally, applying a sequence of actions to derive a parse tree.<\/p>"},{"question":"How is Dependency Parsing used?","answer":"<p>Dependency Parsing is used in several NLP applications like machine translation, where it helps in identifying grammatical relations in the source language, information extraction, where it aids in understanding the meaning of the text, and sentiment analysis, where it helps understand the sentiment of a sentence more accurately.<\/p>"},{"question":"How do Proxy Servers relate to Dependency Parsing?","answer":"<p>While proxy servers don't directly interact with Dependency Parsing, they can be used to facilitate NLP tasks that use this technique. For instance, a proxy server can be used to scrape web data for training NLP models, including those for Dependency Parsing, providing a layer of anonymity that protects the privacy of the individuals or organizations conducting these operations.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/476792","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/476792\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media\/468201"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media?parent=476792"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}